2  Downstream Analysis – feature annotation

2.0.1 Introduction

In this case study, we process a MALDI-TIMS-MS1 lipids dataset of mouse skin tissue, extract features with m/z and ion mobility, then convert ion mobility into collision cross section(CCS), and annotate features by looking up m/z and CCS in CCS compendium.

import timsimaging

# enable visualization in the Jupyter notebook
from bokeh.io import show, output_notebook
output_notebook()
# disable FutureWarning
import warnings
warnings.filterwarnings('ignore', category=FutureWarning)

Loading BokehJS ...

bruker_d_folder_name = r"D:\dataset\Melanie\MF402upper.d"
dataset = timsimaging.spectrum.MSIDataset(bruker_d_folder_name)
dataset

100%|██████████████████████████████████████████████████████████████████████████| 65052/65052 [00:13<00:00, 4783.46it/s]

MSIDataset with 65052 pixels
        mz range: 299.999-1350.004
        mobility range: 0.700-1.960
        

dataset.image()

2.0.2 Peak processing

First we run the processing workflow to get the feature list. There are 3 columns in the result: m/z, ion mobility and intensity. The ion mobility is in \(1/K_0\) (inverse reduced mobility), of which the unit is \(V\cdotp s\cdotp cm^{-2}\)

results = dataset.process(sampling_ratio=0.1, frequency_threshold=0.02, tolerance=3, adaptive_window=True, visualize=False)
peak_list = results["peak_list"]
peak_list

Computing mean spectrum...
Traversing graph...
Finding local maxima...
Summarizing...

100%|████████████████████████████████████████████████████████████████████████████████| 930/930 [02:41<00:00,  5.77it/s]

	mz_values	mobility_values	total_intensity
1	302.058416	0.906070	28.807071
2	303.065771	0.907390	273.366334
3	303.065934	1.076893	73.819985
4	303.065516	0.948773	8.603689
5	304.069069	0.906924	3.887010
...	...	...	...
926	1253.899905	1.844601	14.027978
927	1255.913279	1.853024	26.725288
928	1281.929607	1.870141	25.601230
929	1283.943538	1.877011	2.597694
930	1298.554342	1.759565	7.082859

930 rows × 3 columns

2.0.3 Converting ion mobility into CCS

However, \(1/K_0\) depends on the ion mobility technique and instrument. The data was collected by trapped ion mobility spectrometry(TIMS) while most ion mobility databases are from drift tube(DTIMS), to allow cross-platform comparison, we need to convert ion mobility into CCS, a property only depends on the ion itself.

TIMSImaging converts \(1/K_0\) into CCS by a linear model fitted with reference ion mobilities of calibrants. The metadata of calibration is stored in the raw data:

dataset.cali_info

	KeyPolarity	Value
KeyName
CalibrationDateTime	+	2024-07-28T12:37:35+02:00
CalibrationUser	+	unknown
CalibrationSoftware	+	timsTOF
CalibrationSoftwareVersion	+	5.1.8
MzCalibrationMode	+	4
MzStandardDeviationPPM	+	0.209581
ReferenceMassList	+	RedP_0_to_2000 07-03-2024 New List
MzCalibrationSpectrumDescription	+	<unknown>
ReferenceMassPeakNames	+	b'p3\x00p5\x00p9\x00p17\x00p21\x00p25\x00p29\x...
ReferencePeakMasses	+	b'\x17\xd9\xce\xf7S"\x8a@\xc5 \xb0r\xe8\x11\x8...
MeasuredTimesOfFlight	+	b'!\xe8\xa9\xb9\x14\x0c\xf2@e\xaeo\xd5\x7f\xb3...
MeasuredMassPeakIntensities	+	b'\x00\x00\x00\x00\x80<\xc6@\x00\x00\x00\x00\x...
MassesPreviousCalibration	+	b'\xe0\xf4\xb4cY"\x8a@\xd79uE\xec\x11\x8c@\x7f...
MassesCorrectedCalibration	+	b'\x962\xe9BT"\x8a@B\x8e\xd8\xe1\xe7\x11\x8c@\...
MobilityCalibrationDateTime	+	2024-07-27T13:42:36+02:00
MobilityCalibrationUser	+	unknown
MobilityStandardDeviationPercent	+	0.175188
ReferenceMobilityList	+	Tuning Mix ES-TOF CCS compendium (ESI)
CalibrationMobilogramDescription	+	<unknown>
ReferenceMobilityPeakNames	+	b'C6H19N3O6P3\x00C12H19F12N3O6P3\x00C18H19F24N...
ReferencePeakMobilities	+	b'\xbe\xa3\x80$\x12\x90\xe7?\x00\xa0\xefq\x07\...
MeasuredTimsVoltages	+	b':\xe9\xb9o{uY@\x9d\xa9\x10\x8a\x9ada@\x8a:0,...
MeasuredMobilityPeakIntensities	+	b'\x0eJ\x96\x80\x13\xe1CA\xdce\xc5&\xb0\x9feA\...
MobilitiesPreviousCalibration	+	b'\x8a^\x97]\xd1\x9e\xe7?\xfd\xdap2s\xd5\xef?\...
MobilitiesCorrectedCalibration	+	b'\xb5\x1aw\xe9\xb5\x8c\xe7?"d\xaaG\x9a\xc6\xe...
MobilitiyReferencePressure	+	2.195872
MobilitiyPressureCompensationFactor	+	50.000000

Compute CCS. We also have an option to call interal CCS conversion function from Bruker’s TDFSDK API. Here we compute CCS in both methods.

ccs_linear_model = dataset.ccs_calibrator(method="linear")
peak_list["CCS_linear"] = ccs_linear_model.transform(peak_list["mz_values"], peak_list["mobility_values"], charge=1)
ccs_bruker_model = dataset.ccs_calibrator(method="internal")
peak_list["CCS_bruker"] = ccs_bruker_model.transform(peak_list["mz_values"], peak_list["mobility_values"], charge=1)
peak_list["Delta"] = (peak_list["CCS_bruker"]-peak_list["CCS_linear"])/peak_list["CCS_bruker"]

peak_list

	mz_values	mobility_values	total_intensity	CCS_linear	CCS_bruker	Delta
1	302.058416	0.906070	28.807071	189.238376	189.666498	0.002257
2	303.065771	0.907390	273.366334	189.489590	189.916152	0.002246
3	303.065934	1.076893	73.819985	225.188273	225.393028	0.000908
4	303.065516	0.948773	8.603689	198.205047	198.577456	0.001875
5	304.069069	0.906924	3.887010	189.364654	189.791992	0.002252
...	...	...	...	...	...	...
926	1253.899905	1.844601	14.027978	374.204010	373.482918	-0.001931
927	1255.913279	1.853024	26.725288	375.913545	375.181832	-0.001950
928	1281.929607	1.870141	25.601230	379.316539	378.563683	-0.001989
929	1283.943538	1.877011	2.597694	380.709363	379.947853	-0.002004
930	1298.554342	1.759565	7.082859	356.744077	356.131468	-0.001720

930 rows × 6 columns

The results from TIMSImaging are almost the same with those from Bruker’s method.

2.0.4 Query features against CCS database

Now we can search m/z and CCS in a database to obtain putative feature annotation. Here we use CCS compendium, which could be downloaded from https://mcleanresearchgroup.shinyapps.io/CCS-Compendium

import pandas as pd
def query_feature(mz, ccs, i, library, ppm_tol=20, ccs_tol=5):

    columns = ['Compound', 'Neutral.Formula', 'CAS',
        'Theoretical.mz', 'Ion.Species', 'Charge',
        'CCS', 'Super.Class',
        'Class', 'Subclass']
    # ppm window
    mz_tol = mz * ppm_tol * 1e-6
    mz_min, mz_max = mz - mz_tol, mz + mz_tol
    
    ccs_min, ccs_max = ccs - ccs_tol, ccs + ccs_tol
    hit_index =  (library['mz'].between(mz_min, mz_max)) & (library['CCS'].between(ccs_min, ccs_max))
    # candidate subset by adduct and mz
    hits = library.loc[hit_index, columns].copy()
    hits["feature_id"] = i
    hits["measured_mz"] = mz
    hits["measured_CCS"] = ccs
    hits["ppm_error"] = (hits["Theoretical.mz"] - mz) / mz * 1e6
    hits["ccs_dev_%"] = ((hits["CCS"] - ccs) / ccs).abs() * 100

    if hits.empty:
        return pd.DataFrame()
    return hits

To save time, we search against a subset of the database: lipids with +1 charge state:

library = pd.read_csv(r"D:\dataset\UnifiedCCSCompendium_FullDataSet_2025-07-28.csv")
lipids_lib = library.loc[
    (library["Super.Class"]=="Lipids and lipid-like molecules")&
    (library["Charge"]==1)
    ]
lipids_lib

	Compound	Neutral.Formula	CAS	InChi	InChiKey	Theoretical.mz	mz	Ion.Species	Ion.Species.Agilent	Charge	...	Rep5	Rep6	Rep7	Rep8	Rep9	Rep10	Rep11	Rep12	Rep13	Rep14
1045	5-iPF2alpha-VI	C20H34O5	180469-63-0	InChI=1S/C20H34O5/c1-2-3-4-5-6-7-10-16-17(19(2...	RZCPXIZGLPAGEV-UHFFFAOYSA-N	377.2304	377.2304	[M+Na]	(M+Na)+	1	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1047	8-iso-15(R)-Prostaglandin F2alpha	C20H34O5	214748-65-9	InChI=1S/C20H34O5/c1-2-3-6-9-15(21)12-13-17-16...	PXGPLTODNUVGFL-PGWUFSIFSA-N	377.2304	377.2304	[M+Na]	(M+Na)+	1	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1049	8-iso-Prostaglandin F2alpha	C20H34O5	27415-26-5	InChI=1S/C20H34O5/c1-2-3-6-9-15(21)12-13-17-16...	PXGPLTODNUVGFL-NAPLMKITSA-N	377.2304	377.2304	[M+Na]	(M+Na)+	1	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1051	15(R)-Prostaglandin F2alpha	C20H34O5	37658-84-7	InChI=1S/C20H34O5/c1-2-3-6-9-15(21)12-13-17-16...	PXGPLTODNUVGFL-CKXCCYAOSA-N	377.2304	377.2304	[M+Na]	(M+Na)+	1	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1053	11-beta-Prostaglandin F2alpha	C20H34O5	38432-87-0	InChI=1S/C20H34O5/c1-2-3-6-9-15(21)12-13-17-16...	PXGPLTODNUVGFL-ZWAKLXPCSA-N	377.2304	377.2304	[M+Na]	(M+Na)+	1	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3666	Progesterone	C21H30O2	57-83-0	InChI=1S/C21H30O2/c1-13(22)17-6-7-18-16-5-4-14...	RJKFOVLPORLFTN-LEKSSAKUSA-N	337.2144	337.2143	[M+Na]	(M+Na)+	1	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3668	17-alpha-Hydroxyprogesterone	C21H30O3	68-96-2	InChI=1S/C21H30O3/c1-13(22)21(24)11-8-18-16-5-...	DBPWSSGDRRHUNT-CEGNMAFCSA-N	331.2273	331.2273	[M+H]	(M+H)+	1	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3669	17-alpha-Hydroxyprogesterone	C21H30O3	68-96-2	InChI=1S/C21H30O3/c1-13(22)21(24)11-8-18-16-5-...	DBPWSSGDRRHUNT-CEGNMAFCSA-N	353.2093	353.2093	[M+Na]	(M+Na)+	1	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3670	d7-Cholesterol Ester (18:1)	C45H71D7O2	1416275-35-8	InChI=1S/C45H78O2/c1-7-8-9-10-11-12-13-14-15-1...	RJECHNNFRHZQKU-IHPCOYDHSA-N	680.6339	680.6305	[M+Na]	(M+Na)+	1	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3671	Cholesteryl Palmitate	C43H76O2	601-34-3	InChI=1S/C43H76O2/c1-7-8-9-10-11-12-13-14-15-1...	BBJQPKLGPMQWBU-JADYGXMDSA-N	647.5743	647.5704	[M+Na]	(M+Na)+	1	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

664 rows × 37 columns

Query features with m/z tolerance=20 ppm and CCS tolerance=5 \(\mathring{A}^2\):

all_results = []
for i, feat in peak_list.iterrows():
    all_results.append(query_feature(feat['mz_values'], feat['CCS_linear'], i, lipids_lib, ppm_tol=20, ccs_tol=5))

final_results = pd.concat([r for r in all_results if not r.empty],
                              ignore_index=True) if any(not r.empty for r in all_results) else pd.DataFrame()
final_results

	Compound	Neutral.Formula	CAS	Theoretical.mz	Ion.Species	Charge	CCS	Super.Class	Class	Subclass	feature_id	measured_mz	measured_CCS	ppm_error	ccs_dev_%
0	LysoPC (13:0)	C21H44NO7P	20559-17-5	454.2933	[M+H]	1	217.70	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	179	454.302122	212.734417	-19.418093	2.334170
1	Lithocholyltaurine	C26H45NO5S	6042-32-6	506.2916	[M+Na]	1	213.30	Lipids and lipid-like molecules	Steroids and steroid derivatives	Bile acids, alcohols and derivatives	232	506.299203	217.302892	-15.017336	1.842080
2	LysoPC (16:0)	C24H50NO7P	17364-16-8	518.3223	[M+Na]	1	234.30	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	239	518.332071	235.840590	-18.850706	0.653234
3	LysoPC (18:1)	C26H52NO7P	19420-56-5	522.3559	[M+H]	1	233.20	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	245	522.365273	235.736654	-17.942795	1.076054
4	1,2-Didecanoyl-sn-glycero-3-phosphoethanolamine	C25H50NO8P	253685-27-7	524.3352	[M+H]	1	235.40	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphoethanolamines	250	524.344495	236.731846	-17.727345	0.562597
5	LysoPC (18:0)	C26H54NO7P	19420-57-6	524.3716	[M+H]	1	238.80	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	251	524.380728	240.115902	-17.406327	0.548028
6	LysoPC (2-18:0)	C26H54NO7P	27098-24-4	524.3716	[M+H]	1	240.70	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	251	524.380728	240.115902	-17.406327	0.243257
7	Platelet-activating Factor	C26H54NO7P	74389-68-7	524.3716	[M+H]	1	239.30	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	251	524.380728	240.115902	-17.406327	0.339795
8	LysoPC (19:0)	C27H56NO7P	108273-88-7	538.3872	[M+H]	1	240.50	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	262	538.395407	243.788239	-15.242698	1.348810
9	PC (17:0/02:0)	C27H56NO7P	93037-84-4	538.3872	[M+H]	1	244.20	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	262	538.395407	243.788239	-15.242698	0.168901
10	LysoPC (18:1)	C26H52NO7P	19420-56-5	544.3379	[M+Na]	1	236.70	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	268	544.347728	233.680295	-18.055166	1.292238
11	LysoPC (18:0)	C26H54NO7P	19420-57-6	546.3536	[M+Na]	1	241.50	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	272	546.361114	242.741801	-13.753318	0.511573
12	LysoPC (2-18:0)	C26H54NO7P	27098-24-4	546.3536	[M+Na]	1	242.20	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	272	546.361114	242.741801	-13.753318	0.223201
13	Platelet-activating Factor	C26H54NO7P	74389-68-7	546.3536	[M+Na]	1	240.80	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	272	546.361114	242.741801	-13.753318	0.799945
14	PC (16:0E/2:0)	C26H54NO7P	74389-68-8	546.3536	[M+Na]	1	240.60	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	272	546.361114	242.741801	-13.753318	0.882337
15	LysoPC (20:0)	C28H58NO7P	108341-80-6	552.4029	[M+H]	1	247.00	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	281	552.409856	247.601055	-12.591734	0.242752
16	PC (O-18:0/2:0)	C28H58NO7P	74389-69-8	552.4029	[M+H]	1	247.50	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	281	552.409856	247.601055	-12.591734	0.040814
17	1,2-Dilauroyl-sn-glycero-3-phosphocholine	C32H64NO8P	18194-25-7	622.4448	[M+H]	1	260.60	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	358	622.447463	262.186356	-4.278869	0.605049
18	Cer 42:02	C42H81NO3	NaN	630.6189	[M+H-H2O]	1	275.42	Lipids and lipid-like molecules	Sphingolipids	Ceramides	371	630.621440	278.570366	-4.028141	1.130905
19	Cer 42:01	C42H83NO3	NaN	632.6346	[M+H-H2O]	1	277.23	Lipids and lipid-like molecules	Sphingolipids	Ceramides	376	632.637809	281.892273	-5.072188	1.653920
20	1,2-Dilauroyl-sn-glycero-3-phosphocholine	C32H64NO8P	18194-25-7	644.4268	[M+Na]	1	263.70	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	388	644.430335	259.430516	-5.484800	1.645714
21	PE (O-32:01)	C37H74NO7P	NaN	720.4920	[M+2Na-H]	1	269.40	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphoethanolamines	509	720.478763	274.019176	18.372030	1.685713
22	PC (16:1/16:1) (del9-cis)	C40H76NO8P	4724-96-3	730.5387	[M+H]	1	278.40	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	528	730.537578	282.629394	1.536049	1.496445
23	1,2-Dipalmitoyl-sn-glycero-3-phosphocholine	C40H80NO8P	63-89-8	734.5700	[M+H]	1	284.60	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	540	734.568422	289.282809	2.147626	1.618765
24	SM 36:01	C41H83N2O6P	NaN	735.5781	[M+Na-H2O]	1	290.30	Lipids and lipid-like molecules	Sphingolipids	Phosphosphingolipids	541	735.572123	289.413082	8.125111	0.306454
25	1-Palmitoyl-2-oleoyl-sn-glycero-3-phosphoethan...	C39H76NO8P	26662-94-2	740.5207	[M+Na]	1	281.70	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphoethanolamines	545	740.522002	280.097591	-1.757631	0.572090
26	PE 34:01	C39H76NO8P	NaN	740.5207	[M+Na]	1	277.10	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphoethanolamines	545	740.522002	280.097591	-1.757631	1.070195
27	1,2-Diheptadecanoyl-sn-glycero-3-phosphoethano...	C39H78NO8P	140219-78-9	742.5363	[M+Na]	1	282.10	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphoethanolamines	548	742.537248	279.640666	-1.276611	0.879462
28	PE 34:01	C39H76NO8P	NaN	756.4946	[M+K]	1	278.70	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphoethanolamines	572	756.480464	278.396763	18.686354	0.108923
29	1,2-Dipalmitoyl-sn-glycero-3-phosphocholine	C40H80NO8P	63-89-8	756.5520	[M+Na]	1	286.20	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	573	756.552206	287.253995	-0.272643	0.366921
30	PC (18:1(9Z)/16:0)	C42H82NO8P	59491-62-2	760.5856	[M+H]	1	287.90	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	579	760.582752	291.934324	3.744116	1.381929
31	Di(2-ethylhexyl) adipate	C22H42O4	103-23-1	763.6064	[2M+Na]	1	294.70	Lipids and lipid-like molecules	Fatty acyls	Fatty acid esters	585	763.598494	294.424703	10.353029	0.093503
32	PE 36:03	C41H76NO8P	NaN	764.5207	[M+Na]	1	278.70	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphoethanolamines	586	764.521398	282.702581	-0.912694	1.415827
33	PE (18:1/18:1)(del9-cis)	C41H78NO8P	NaN	766.5363	[M+Na]	1	285.80	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphoethanolamines	591	766.535777	283.103817	0.682387	0.952365
34	PE 36:02	C41H78NO8P	NaN	766.5363	[M+Na]	1	281.00	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphoethanolamines	591	766.535777	283.103817	0.682387	0.743126
35	PE (18:1/18:1)(del9-cis)	C41H78NO8P	NaN	766.5363	[M+Na]	1	285.80	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphoethanolamines	592	766.535407	287.758043	1.164832	0.680448
36	GlcCer 36:01 OH	C42H81NO9	NaN	766.5809	[M+Na]	1	288.70	Lipids and lipid-like molecules	Sphingolipids	Glycosphingolipids	593	766.573442	291.006475	9.728708	0.792586
37	PE 36:01	C41H80NO8P	NaN	768.5520	[M+Na]	1	283.30	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphoethanolamines	596	768.552311	287.198305	-0.404387	1.357356
38	SM 36:01	C41H83N2O6P	NaN	769.5626	[M+K]	1	288.30	Lipids and lipid-like molecules	Sphingolipids	Phosphosphingolipids	598	769.555997	286.955141	8.580261	0.468665
39	1,2-Distearoyl-sn-glycero-3-phosphoethanolamine	C41H82NO8P	1069-79-0	770.5676	[M+Na]	1	289.80	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphoethanolamines	600	770.567181	289.246640	0.543238	0.191311
40	PC 34:03	C42H78NO8P	NaN	778.5363	[M+Na]	1	283.10	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	616	778.536287	287.489106	0.016594	1.526703
41	PC (18:1(9Z)/16:0)	C42H82NO8P	59491-62-2	782.5676	[M+Na]	1	292.10	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	622	782.567462	291.542761	0.176267	0.191135
42	PC (18:1/18:1) (del9-trans)	C44H84NO8P	56782-46-8	786.6013	[M+H]	1	293.30	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	629	786.598330	295.581854	3.775213	0.771987
43	PE 36:01	C41H80NO8P	NaN	790.5339	[M+2Na-H]	1	284.90	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphoethanolamines	638	790.537247	285.507623	-4.233978	0.212822
44	1,2-Distearoyl-sn-glycero-3-phosphocholine	C44H88NO8P	816-94-4	790.6326	[M+H]	1	297.60	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	640	790.622388	299.132978	12.916392	0.512474
45	GlcCer 38:01 OH	C44H85NO9	NaN	794.6122	[M+Na]	1	294.20	Lipids and lipid-like molecules	Sphingolipids	Glycosphingolipids	650	794.603740	297.520120	10.646353	1.115931
46	PC 34:02	C42H80NO8P	NaN	796.5259	[M+K]	1	284.40	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	654	796.510867	285.289684	18.873542	0.311853
47	PE (O-38:05)	C43H78NO7P	NaN	796.5233	[M+2Na-H]	1	283.60	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphoethanolamines	654	796.510867	285.289684	15.609305	0.592270
48	PC (18:1/18:1) (del9-trans)	C44H84NO8P	56782-46-8	808.5833	[M+Na]	1	296.90	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	680	808.578809	295.966653	5.553767	0.315355
49	1,2-Distearoyl-sn-glycero-3-phosphocholine	C44H88NO8P	816-94-4	812.6146	[M+Na]	1	298.60	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	688	812.611023	299.193222	4.401900	0.198274
50	d7-TG (15:0-18:1)	C51H89D7O6	2097561-17-4	812.7724	[M+H]	1	302.00	Lipids and lipid-like molecules	Glycerolipids	Monoradylglycerols	689	812.670574	305.824841	125.297981	1.250664
51	SM 42:02	C47H93N2O6P	NaN	817.6563	[M+Na-H2O]	1	305.60	Lipids and lipid-like molecules	Sphingolipids	Phosphosphingolipids	699	817.649049	304.204308	8.868113	0.458801
52	SM 42:00	C47H97N2O6P	NaN	817.7162	[M+H]	1	306.50	Lipids and lipid-like molecules	Sphingolipids	Phosphosphingolipids	700	817.705902	311.478752	12.593910	1.598424
53	PS 36:04	C42H74NO10P	NaN	828.4767	[M+2Na-H]	1	283.80	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphoserines	717	828.463720	281.481017	15.667531	0.823851
54	PE 39:02	C44H84NO8P	NaN	830.5652	[M+2Na-H]	1	293.00	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphoethanolamines	724	830.563142	296.932805	2.477361	1.324477
55	PS 37:04	C43H76NO10P	NaN	842.4924	[M+2Na-H]	1	288.10	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphoserines	744	842.479425	285.163722	15.400504	1.029681
56	1,2-Diarachidoyl-sn-glycero-3-phosphocholine	C48H96NO8P	61596-53-0	846.6952	[M+H]	1	308.00	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	753	846.685887	311.941168	10.999307	1.263433
57	1,2-Diarachidoyl-sn-glycero-3-phosphocholine	C48H96NO8P	61596-53-0	868.6772	[M+Na]	1	310.70	Lipids and lipid-like molecules	Glycerophospholipids	Glycerophosphocholines	777	868.676053	310.987409	1.320390	0.092418
58	Diisononyl hexahydrophthalate	C26H48O4	166412-78-8	871.7003	[2M+Na]	1	316.70	Lipids and lipid-like molecules	Fatty acyls	Fatty alcohol esters	784	871.695489	313.453756	5.518589	1.035637

We got some matches and we can view their ion images:

image, _  = timsimaging.plotting.image(dataset, i=539, results=results)
show(image)

2.0.5 Compare with annotation results from METASPACE

Here we compare using LIPID MAPS

metaspace_results = pd.read_csv(r"D:\dataset\Melanie\metaspace_annotations.csv", header=2)
metaspace_results

	group	datasetName	datasetId	formula	adduct	chemMod	ion	mz	msm	fdr	...	moleculeIds	minIntensity	maxIntensity	totalIntensity	isomeric_mols_count	isomeric_ions_count	isobaric_ions_count	offSample	rawOffSampleProb	isobarIons
0	NaN	MF402upper_noim	2026-01-07_13h02m29s	C40H80NO8P	M+H	NaN	C40H80NO8P+H+	734.569432	0.932096	0.05	...	LMGP01010418, LMGP01010463, LMGP01010488, LMGP...	0	43689	492517728	25	0	0	NaN	NaN	NaN
1	NaN	MF402upper_noim	2026-01-07_13h02m29s	C38H77N2O7P	M+H	NaN	C38H77N2O7P+H+	705.554116	0.903219	0.05	...	LMSP03020062, LMSP03020082	0	11954	51471516	2	0	0	NaN	NaN	NaN
2	NaN	MF402upper_noim	2026-01-07_13h02m29s	C41H83N2O6P	M+H	NaN	C41H83N2O6P+H+	731.606152	0.852665	0.05	...	LMSP03010052, LMSP03020018, LMSP03010001, LMSP...	0	11995	113299328	6	0	0	NaN	NaN	NaN
3	NaN	MF402upper_noim	2026-01-07_13h02m29s	C45H91N2O6P	M+H	NaN	C45H91N2O6P+H+	787.668752	0.848176	0.05	...	LMSP03010073, LMSP03010006	0	10226	91480856	2	0	0	NaN	NaN	NaN
4	NaN	MF402upper_noim	2026-01-07_13h02m29s	C33H64NO9P	M+H	NaN	C33H64NO9P+H+	650.439146	0.826815	0.05	...	LMGP20010008	0	12940	90582144	1	0	0	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
91	NaN	MF402upper_noim	2026-01-07_13h02m29s	C35H64NO10P	M+H	NaN	C35H64NO10P+H+	690.434061	0.307145	0.10	...	LMGP03010164, LMGP03010278, LMGP03010052, LMGP...	0	1975	15041466	4	0	0	NaN	NaN	NaN
92	NaN	MF402upper_noim	2026-01-07_13h02m29s	C36H68NO10P	M+H	NaN	C36H68NO10P+H+	706.465361	0.300800	0.10	...	LMGP03010165, LMGP03010203, LMGP03010249, LMGP...	0	1686	11925794	10	0	1	NaN	NaN	C38H69NO8+K+
93	NaN	MF402upper_noim	2026-01-07_13h02m29s	C40H81N2O6P	M+H	NaN	C40H81N2O6P+H+	717.590502	0.239693	0.10	...	LMSP03010044, LMSP03010045, LMSP03020024, LMSP...	0	1097	10183518	4	0	0	NaN	NaN	NaN
94	NaN	MF402upper_noim	2026-01-07_13h02m29s	C39H74NO8P	M+H	NaN	C39H74NO8P+H+	716.522482	0.228522	0.10	...	LMGP01011387, LMGP01011478, LMGP02010416, LMGP...	0	1327	8856583	24	0	0	NaN	NaN	NaN
95	NaN	MF402upper_noim	2026-01-07_13h02m29s	C46H80NO10P	M+H	NaN	C46H80NO10P+H+	838.559261	0.205566	0.10	...	LMGP03010339, LMGP03010422, LMGP03010580, LMGP...	0	1455	7769442	14	0	1	NaN	NaN	C44H82NO10P+Na+

96 rows × 24 columns

metaspace_ions = metaspace_results[["formula", "adduct"]].copy()
metaspace_ions["adduct"] = metaspace_ions["adduct"].map(lambda x: f"[{x}]".format(x))
timsimaging_ions = final_results[["Neutral.Formula", "Ion.Species"]].copy()
timsimaging_ions.columns = ["formula", "adduct"]
intersection = timsimaging_ions.merge(
    metaspace_ions,
    on=["formula", "adduct"],
    how="inner"
)
intersection

	formula	adduct
0	C40H76NO8P	[M+H]
1	C40H80NO8P	[M+H]
2	C39H78NO8P	[M+Na]
3	C40H80NO8P	[M+Na]
4	C41H76NO8P	[M+Na]
5	C41H80NO8P	[M+Na]
6	C41H82NO8P	[M+Na]
7	C42H78NO8P	[M+Na]
8	C42H82NO8P	[M+Na]
9	C48H96NO8P	[M+Na]

{#ion image from metaspace width=“60%”}